{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 糖尿病发病预测\n",
    "pregnants：怀孕次数\n",
    "Plasma_glucose_concentration：口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度\n",
    "blood_pressure：舒张压，单位:mm Hg\n",
    "Triceps_skin_fold_thickness：三头肌皮褶厚度，单位：mm\n",
    "serum_insulin：餐后血清胰岛素，单位:mm\n",
    "BMI：体重指数（体重（公斤）/ 身高（米）^2）\n",
    "Diabetes_pedigree_function：糖尿病家系作用\n",
    "Age：年龄\n",
    "Target：标签， 0表示不发病，1表示发病"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1、导入工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2、读取数据\n",
    "train = pd.read_csv('pima-indians-diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAE9RJREFUeJzt3X/wXXV95/HnKwQsgy7hR2DYJDRQs45OrYiRpaVrK9hWdJcgK9ouLVEzm3YGLF26XdFtpS27rbb1R6lb2kyxhq5KKRWJwqo0Ak7tSgmg/BAdIoMkG5dEBARZfwTe+8f9fOEaTr7fE8L93sv3+3zM3LnnfM7n3Pu+TOb74pzPOZ+TqkKSpF0tGHcBkqTJZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSeq0cNwF7I1DDz20li9fPu4yJOlZ5aabbvpmVS2eqd+zOiCWL1/Opk2bxl2GJD2rJPl6n36eYpIkdTIgJEmdDAhJUicDQpLUyYCQJHUaaUAkuSfJbUm+mGRTazs4yTVJ7mrvB7X2JLkwyeYktyY5dpS1SZKmNxtHEK+sqmOqamVbPw/YWFUrgI1tHeBkYEV7rQUumoXaJEm7MY5TTKuA9W15PXDqUPslNfAFYFGSI8ZQnySJ0QdEAZ9JclOSta3t8Kr6BkB7P6y1LwG2DO27tbVJksZg1HdSn1BV25IcBlyT5CvT9E1HWz2l0yBo1gIceeSRe13gy37rkr3+DM09N/3xmeMuQRq7kR5BVNW29r4duAI4Drhv6tRRe9/eum8Flg3tvhTY1vGZ66pqZVWtXLx4xqlEJElP08gCIskBSZ43tQz8PHA7sAFY3bqtBq5syxuAM9vVTMcDD02dipIkzb5RnmI6HLgiydT3fKSqPpXkRuCyJGuAe4HTW/+rgdcAm4FHgTePsDZJ0gxGFhBVdTfwko72+4GTOtoLOGtU9UiS9ox3UkuSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6jTygEiyT5JbknyyrR+V5IYkdyX52yT7tfbntPXNbfvyUdcmSdq92TiCOAe4c2j93cD7qmoF8ACwprWvAR6oqucD72v9JEljMtKASLIUeC3wV209wInA5a3LeuDUtryqrdO2n9T6S5LGYNRHEO8H/gvweFs/BHiwqna29a3Akra8BNgC0LY/1PpLksZgxoBIckCSBW35XyU5Jcm+Pfb7t8D2qrppuLmja/XYNvy5a5NsSrJpx44dM5UhSXqa+hxBfA74kSRLgI3Am4EP9djvBOCUJPcAlzI4tfR+YFGSha3PUmBbW94KLANo2w8EvrXrh1bVuqpaWVUrFy9e3KMMSdLT0ScgUlWPAqcBf1ZVrwNeNNNOVfX2qlpaVcuBXwQ+W1VnANcCr2/dVgNXtuUNbZ22/bNV9ZQjCEnS7OgVEEl+EjgDuKq1LZym/0zeBpybZDODMYaLW/vFwCGt/VzgvL34DknSXurzh/43gLcDV1TVHUmOZnAU0FtVXQdc15bvBo7r6PNd4PQ9+VxJ0ujMGBBVdT1wfZID2vrdwK+PujBJ0nj1uYrpJ5N8mXazW5KXJPnzkVcmSRqrPmMQ7wd+AbgfoKq+BLxilEVJksav141yVbVll6bHRlCLJGmC9Bmk3pLkp4BqE+v9Oj88t5IkaQ7qcwTxa8BZDKbC2Aoc09YlSXNYn6uYvsngHghJ0jzS5yqm9UkWDa0flOSDoy1LkjRufU4x/URVPTi1UlUPAC8dXUmSpEnQJyAWJDloaiXJwezdVBuSpGeBPn/o3wP8U5Kph/ycDvz30ZUkSZoEfQapL0lyE/BKBs9sOK2qvjzyyiRJY9X3VNFXGDw/eiFAkiOr6t6RVSVJGrsZAyLJW4HzgfsY3EEdBk96+4nRliZJGqc+RxDnAC+oqvtHXYwkaXL0uYppC/DQqAuRJE2WPkcQdwPXJbkK+N5UY1W9d2RVSZLGrk9A3Nte+7WXJGke6HOZ6+8BJDmgqr4z+pIkSZPAJ8pJkjr5RDlJUiefKCdJ6uQT5SRJnXyinCSp07RHEEn2AX6lqnyinCTNM9MeQVTVY8CqWapFkjRB+oxBfD7JB4C/BZ64D6Kqbh5ZVZKksesTED/V3n9/qK2AE5/5ciRJk2KmMYgFwEVVddks1SNJmhAzjUE8Dpw9S7VIkiZIn8tcr0nyn5MsS3Lw1GvklUmSxqrPGMRb2vvwvQ8FHP3MlyNJmhR9ZnM9ajYKkSRNlj7PpD6zq72qLplhvx8BPgc8p33P5VV1fpKjgEuBg4GbGdyI9/0kzwEuAV7GYGLAN1bVPXvwWyRJz6A+YxAvH3r9G+B3gVN67Pc94MSqegmD6TleneR44N3A+6pqBfAAsKb1XwM8UFXPB97X+kmSxqTPKaa3Dq8nORD4mx77FfBIW923vabun/gPrX09g8C5iMEd27/b2i8HPpAk7XMkSbOs13Tfu3gUWNGnY5J9knwR2A5cA3wNeLCqdrYuWxlMAkh73wLQtj8EHPI06pMkPQP6jEF8gsH/+cMgUF4E9Lpxrs3ldEySRcAVwAu7uk191TTbhutZC6wFOPLII/uUIUl6Gvpc5vonQ8s7ga9X1dY9+ZKqejDJdcDxwKIkC9tRwlJgW+u2FVgGbE2yEDgQ+FbHZ60D1gGsXLnS00+SNCJ9TjHdC9xQVddX1eeB+5Msn2mnJIvbkQNJ9gdexeBBQ9cCr2/dVgNXtuUNbZ22/bOOP0jS+PQJiL8DHh9af6y1zeQI4NoktwI3AtdU1SeBtwHnJtnMYIzh4tb/YuCQ1n4ucF6/nyBJGoU+p5gWVtX3p1baPQv7zbRTVd0KvLSj/W7guI727wKn96hHkjQL+hxB7EjyxH0PSVYB3xxdSZKkSdDnCOLXgA+3hwbBYDC58+5qSdLc0edGua8Bxyd5LpCqenj0ZUmSxm3GU0xJ/iDJoqp6pKoeTnJQkv82G8VJksanzxjEyVX14NRKVT0AvGZ0JUmSJkGfgNinzbQKPHFPw3Om6S9JmgP6DFL/T2Bjkr9mMPXFWxhMsidJmsP6DFL/UbvZ7VWt6YKq+vRoy5IkjVufIwiAW3hyuu5bRleOpCn3/v6Lx12CJtCR77xt1r6rz1VMbwD+mcH8SG8Abkjy+un3kiQ92/U5gvivwMurajsMJuED/oHBQ30kSXNUn6uYFkyFQ3N/z/0kSc9ifY4gPpXk08BH2/obgatHV5IkaRL0uYrpt5KcBvw0g6e+rauqK0ZemSRprHpdxVRVHwM+NuJaJEkTxLEESVInA0KS1Gm3AZFkY3t/9+yVI0maFNONQRyR5GeAU5JcymCA+glVdfNIK5MkjdV0AfFO4DxgKfDeXbYVcOKoipIkjd9uA6KqLgcuT/I7VXXBLNYkSZoAfe6DuCDJKcArWtN1VfXJ0ZYlSRq3PpP1/SFwDvDl9jqntUmS5rA+N8q9Fjimqh4HSLKewZTfbx9lYZKk8ep7H8SioeUDR1GIJGmy9DmC+EPgliTXMrjU9RV49CBJc16fQeqPJrkOeDmDgHhbVf3fURcmSRqvvpP1fQPYMOJaJEkTxLmYJEmdDAhJUqdpAyLJgiS3z1YxkqTJMW1AtHsfvpTkyFmqR5I0IfqcYjoCuCPJxiQbpl4z7ZRkWZJrk9yZ5I4k57T2g5Nck+Su9n5Qa0+SC5NsTnJrkmP37qdJkvZGn6uYfu9pfvZO4Der6uYkzwNuSnIN8CZgY1W9K8l5DGaMfRtwMrCivf41cFF7lySNwYxHEFV1PXAPsG9bvhGY8VkQVfWNqWdGVNXDwJ3AEmAVsL51Ww+c2pZXAZfUwBeARUmO2LOfI0l6pvSZrO8/ApcDf9malgAf35MvSbIceClwA3B4u69i6v6Kw4Y+d8vQbltbmyRpDPqMQZwFnAB8G6Cq7uLJP+ozSvJc4O+B36iqb0/XtaOtOj5vbZJNSTbt2LGjbxmSpD3UJyC+V1Xfn1pJspCOP9xdkuzLIBw+XFUfa833TZ06au/bW/tWYNnQ7kuBbbt+ZlWtq6qVVbVy8eLFfcqQJD0NfQLi+iTvAPZP8nPA3wGfmGmnJAEuBu6squFHlm4AVrfl1cCVQ+1ntquZjgcemjoVJUmafX2uYjoPWAPcBvwqcDXwVz32OwH4FeC2JF9sbe8A3gVclmQNcC9wett2NfAaYDPwKPDmnr9BkjQCfWZzfbw9JOgGBqeWvlpVM55iqqp/pHtcAeCkjv7FYLxDkjQBZgyIJK8F/gL4GoM/+Ecl+dWq+l+jLk6SND59TjG9B3hlVW0GSPJjwFWAASFJc1ifQertU+HQ3M2TVx5Jkuao3R5BJDmtLd6R5GrgMgZjEKczuJtakjSHTXeK6d8NLd8H/Exb3gEcNLKKJEkTYbcBUVVeZipJ81ifq5iOAt4KLB/uX1WnjK4sSdK49bmK6eMM7oj+BPD4aMuRJE2KPgHx3aq6cOSVSJImSp+A+NMk5wOfAb431Tj1rAdJ0tzUJyBezGBOpRN58hRTtXVJ0hzVJyBeBxw9POW3JGnu63Mn9ZeARaMuRJI0WfocQRwOfCXJjfzwGISXuUrSHNYnIM4feRWSpInT53kQ189GIZKkydLnTuqHefIZ1PsB+wLfqap/McrCJEnj1ecI4nnD60lOBY4bWUWSpInQ5yqmH1JVH8d7ICRpzutzium0odUFwEqePOUkSZqj+lzFNPxciJ3APcCqkVQjSZoYfcYgfC6EJM1D0z1y9J3T7FdVdcEI6pEkTYjpjiC+09F2ALAGOAQwICRpDpvukaPvmVpO8jzgHODNwKXAe3a3nyRpbph2DCLJwcC5wBnAeuDYqnpgNgqTJI3XdGMQfwycBqwDXlxVj8xaVZKksZvuRrnfBP4l8NvAtiTfbq+Hk3x7dsqTJI3LdGMQe3yXtSRp7jAEJEmdDAhJUicDQpLUyYCQJHUaWUAk+WCS7UluH2o7OMk1Se5q7we19iS5MMnmJLcmOXZUdUmS+hnlEcSHgFfv0nYesLGqVgAb2zrAycCK9loLXDTCuiRJPYwsIKrqc8C3dmlexeCObNr7qUPtl9TAF4BFSY4YVW2SpJnN9hjE4VX1DYD2flhrXwJsGeq3tbU9RZK1STYl2bRjx46RFitJ89mkDFKno63zqXVVta6qVlbVysWLF4+4LEmav2Y7IO6bOnXU3re39q3AsqF+S4Fts1ybJGnIbAfEBmB1W14NXDnUfma7mul44KGpU1GSpPHo80zqpyXJR4GfBQ5NshU4H3gXcFmSNcC9wOmt+9XAa4DNwKMMnjshSRqjkQVEVf3Sbjad1NG3gLNGVYskac9NyiC1JGnCGBCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqNFEBkeTVSb6aZHOS88ZdjyTNZxMTEEn2Af4HcDLwIuCXkrxovFVJ0vw1MQEBHAdsrqq7q+r7wKXAqjHXJEnz1iQFxBJgy9D61tYmSRqDheMuYEg62uopnZK1wNq2+kiSr460qvnlUOCb4y5iEuRPVo+7BP0w/21OOb/rT+Ue+9E+nSYpILYCy4bWlwLbdu1UVeuAdbNV1HySZFNVrRx3HdKu/Lc5HpN0iulGYEWSo5LsB/wisGHMNUnSvDUxRxBVtTPJ2cCngX2AD1bVHWMuS5LmrYkJCICquhq4etx1zGOeutOk8t/mGKTqKePAkiRN1BiEJGmCGBByihNNrCQfTLI9ye3jrmU+MiDmOac40YT7EPDqcRcxXxkQcooTTayq+hzwrXHXMV8ZEHKKE0mdDAj1muJE0vxjQKjXFCeS5h8DQk5xIqmTATHPVdVOYGqKkzuBy5ziRJMiyUeB/w28IMnWJGvGXdN84p3UkqROHkFIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCa95I8luSLSe5I8qUk5yZZ0LatTHLhDPu/KckH9vA737E3NUuzwctcNe8leaSqntuWDwM+Any+qs7vuf+bgJVVdfbT+U5pUnkEIQ2pqu3AWuDsDPxskk8CJDkuyT8luaW9v2Bo12VJPtWeq/FEsCT55ST/3I5Q/jLJPkneBezf2j48Tb99knwoye1Jbkvyn2bzv4U0Uc+kliZBVd3dTjEdtsumrwCvqKqdSV4F/AHw79u244AfBx4FbkxyFfAd4I3ACVX1gyR/DpxRVeclObuqjgFI8sKufsAdwJKq+vHWb9Eof7e0KwNC6tY1y+2BwPokKxjMeLvv0LZrqup+gCQfA34a2Am8jEFgAOwPbO/43JN20+8TwNFJ/gy4CvjM3v8sqT8DQtpFkqOBxxj8kX7h0KYLgGur6nVJlgPXDW3bdTCvGITM+qp6+0xfubt+SV4C/AJwFvAG4C29f4i0lxyDkIYkWQz8BfCBeuoVHAcC/6ctv2mXbT+X5OAk+wOnAp8HNgKvbwPftO0/2vr/IMnUEUhnvySHAguq6u+B3wGOfcZ+qNSDRxBSGzBmcMpoJ/A3wHs7+v0Rg1NM5wKf3WXbP7b9ng98pKo2AST5beAzbUzjBwyOBL4OrANuTXJzVZ2xm37/D/jrqUtugZmORKRnlJe5SpI6eYpJktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVKn/w9R0THCocpGaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3、各特征分布探索\n",
    "#target\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pregnants\n",
    "fig = plt.figure()\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db0332780>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plasma_glucose_concentration\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#blood_pressure\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'blood_pressure')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"Target\",y='blood_pressure',data=train, hue=\"Target\")\n",
    "plt.xlabel(\"Target\")\n",
    "plt.ylabel(\"blood_pressure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db76192e8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Triceps_skin_fold_thickness\n",
    "fig=plt.figure()\n",
    "sns.distplot(train.Triceps_skin_fold_thickness,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db8ab1d30>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='Triceps_skin_fold_thickness',data=train,hue='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db7b50c50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#serum_insulin\n",
    "fig=plt.figure()\n",
    "sns.distplot(train.serum_insulin,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db8afdf28>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lNTT6jIbEiUTkYuARoG/oOAGMMUYn1LVTUzeTZoRSKSkcEk6vkGMNdjvl5eUluarEiGaexGTgfmAloGM2Y+jYAn9JLkQp1aL8/HwAGnxCjs9y3LbOLpqQqDXG/F/cKlHodWulUlM4EJw+od4nx23r7KIJicUi8gTBmwy5wxuNMatiXlWa0ZaEUqktHAj1XkvTIn8aEic7P/Q4qtk2A1wRu3LSkw6BVSq1NW9JNGh3U8uMMZfHs5B0Fp5MpxGhVGpqunDtE7K9cty2zu60ISEiPzDGvCoiP2/pfWPM/8a+rPTStFS4zpNQKiVZrVYyM+w0+oXG0FyJnJycJFeVGK1pSYTjMj3aVkkQDomAdjcplbKyMjNx+wV3aGynzpMIMcb8NfT46Kn2E5FfGmP+O1aFpZOm7iYNCaVSlt1uxxcAnwm2JGy2aC7pdlztuTPdiW6K9IaIWEVktYjMCb3uJyKfichWEZkhIhmh7Zmh15Wh98tjWF/KCq9Lr6vBKpW6bDYbfiP4TXAxTqvVmuySEiKWIXGqDvWfApuavf4D8EdjzCDgKHBXaPtdwFFjzEDgj6H9Or1wOPj9GhJKpaqACSAYLKRXqz+WIdHiv5qIlAHjgZdCr4XgsNk3Qru8AtwQen596DWh98dIGqyfHe5uCujNh5RKWX5/AIscu4VputwsLBEtiaeB/weElzotBmqa3fq0CugVet4L2A0Qer82tH+nFv5mS5dvOqU6IrfbTabVkBGaTOd2u09zROcQy5B4/cQNInItUG2MWdl8cwvHmla81/xz7xGRFSKy4uDBg20qNpU0tSQCgbRqxirVURhjcDU2Ullr44vDwftIOJ3OJFeVGNGsAtsP+DFQ3vw4Y8x1ocfftXDYxcB1IjIOyAK6EGxZFIqILdRaKAP2hvavAnoDVSJiAwqAIyd+qDFmEjAJYNSoUR3+t2rzFkQgEEibC2JKdRROpxO/P0CD14LbH/yVU1dXR7du3ZJcWfxFM4brLYIrwb7Dsa6jUzLG/BL4JYCIfB34hTHm+yLyOnAjMB24DXg7dMjs0OulofcXmTT407p5SPj9fg0JpVJMTU0NAFYL2CzHb+vsogmJRmPMn2J03n8HpovIb4HVBMOH0OM/RKSSYAvi5hidL6Wd2JJQSqWWQ4cOAWATg03Mcds6u2hC4hkReRh4jzasAmuM+QD4IPR8OzC6hX0aOcV8i86qeUjoXAmlUk91dTUANotpakmEt3V20YTEWcAPCQ5fDf+5q6vAxoDH42l6Hp5Yp5RKHXv3Bi+b2i3BIbBdMqVpW2cXTUhMAPobYzyn3VNFpXlINH+ulEoNe/fupWvWsTkS3bO87NlTldyiEiSaIbBfAIXxKiSdNTa6mz1vTGIlSqmW7Nz5JWdkH2vl98jxsXvnziRWlDjRhER3YLOIzBeR2eGveBWWTpxOZ9NkEJfLldRalFLHM8awa+dOeuYcu3bYM8fPkZpaHA5HEitLjGi6mx6OWxVprr6+HpORi3gaaGhoSHY5SqlmqqurcboaKcvzUdUQHJ5elhsMjB07dnD22Wcns7y4i+bOdB/Gs5B0Vt9QTyAjD4ungfr6+mSXo5RqZvv27QD0yj3WkijLS5+QaHV3k4g4RKQu9NUoIn4RqYtncenAGIPL6cRk5gGkRfNVqY5k27ZtAPTOOxYSRZkBcjOEysrKZJWVMNG0JI67M52I3EALcx1UdBoaGggEAgQyuwAaEkqlmsrKSkpzIMd2bPEHEeiT42Hr1i1JrCwx2rzAnzHmLXSORLuFQyGQkQti0ZBQKsVsqdhE39yTV3ztm+9j+7btnX4CbDQL/H2r2UsLMIoI95BQrVdbWwuAsWUh9izq6rQHT6lU4XA42LvvABf1P3kZ//J8H57dXnbu3MmAAQOSUF1iRDO66ZvNnvuALwneJEi1Q1Mo2DIxtkwNCaVSyJYtwe6k/l1Obi30yw9uq6io0JAQESuw1hjzxzjXk3bCoWBsmfitGU0tC6VU8m3evBkIthpO1D0nQLY9uM+4ceMSXVrCtOqahDHGD1wX51rSUvOQCFgzqanRkFAqVWzcuJEeuYY8+8k96xaB/nleNm7ckITKEieaC9efisizInKpiJwb/opbZWni2DWJTIwti1rtblIqJRhj2LhhPf3zIq+nNqDAy/btOzr1SgnRXJO4KPT4WLNtugpsOzkcDsSWCWLB2DJx1NRhjEEk0i3DlVKJsGfPHo7W1HLm4MgrM59Z4CMQCLBx40ZGjhyZwOoSJ5p5EpfHs5B0VVdXB/bM4AtbJn6fD7fbTVZWVnILUyrNrV27FoDBhZGHuA4s8CES3LezhkQ0M667i8hkEfm/0OuhInJX/EpLD/X19fgtGQAYa/BRRzgplXyrVq2iIJPjFvY7UY7N0C/fz6qVKxNYWWJFc01iCjAf6Bl6vQX4WawLSjd1DgcBix04FhK6fpNSyRUIBFjx+XK+UujmdD2/w7q62bhpY6ddnDOakOhmjJlJ6K50xhgfEDliVavU1zdgrOGQCD525otgSnUEFRUV1NTWMaL49DcBO7vYi98f4PPPP09AZYkXTUg0iEgxoVnWInIBoOM128npdEIoHAi1JDrrXyRKdRRLlixBBM4qPv3thAd28ZGXETymM4pmdNPPgdnAABH5BCgBboxLVWnE6XRicouBYy0Jp9OZzJKUSmvGGBYtfJ9hXb3ktzA/4kRWC4zq1sgnSz6msbGx0w06iaYlMQAYS3Ao7HxgK9GFjDqBMYZGlxNL/UEydy1ruiahLQmlkmfDhg3s23+AC0pPXtQvkgtK3bga3XzyySdxrCw5ogmJ/zTG1AFdgSuBScALcakqTTidTgKBABafG4vzCMYWHAqrK8EqlTxz5swhywajowiJIV19lGQb5sx5J46VJUc0IRG+SD0e+Isx5m0gI/YlpY8jR44AYCyh/w0WG2K1NW1XSiVWTU0Nixct5ILSRrKi6CexCHztDBerV69h586d8SswCaIJiT0i8lfgO8C7IpIZ5fHqBAcOHAg+EWvoUTAZuce2K6USavbs2bg9Xq4qa4z62K/3bMRuhZkzZ8ahsuSJ5pf8dwhei7jGGFMDFAEPxqWqNLFr167gE4u1aZsvows7d+5KUkVKpS+Xy8WsN17n7GJv0z2so9Elw3Bpj0bemz+P6urqOFSYHK0OCWOM0xgzyxizNfR6nzHmvfiV1vlVVlYi9iyMHAsJf05Xdu3ehdvd+v5QpVT7vfXWW9TUObi+vO2jC8f3cRHw+5k2bVoMK0su7S5KorVr1+HNKYFmMzr9eaUE/P6mdeyVUvFXX1/Pa9OmclaRl0EFbb8daUl2gMvOaGTunHfYt29fDCtMHg2JJKmurqaqaje+/B7HbffndQcRVqxYkaTKlEo/06dPp85Rz4392z/8/PpyFxg/L7/8cgwqS764hoSI9BaRxSKySUQ2iMhPQ9uLRGSBiGwNPXYNbRcR+ZOIVIrI2s58v4qlS5cC4C8sO/4NWyaBvFI++fTTJFSlVPo5ePAgr8+cwQXd3fTr0v6VhoqyAlzVy8X77y9ouv1pRxbvloQPeMAY8xXgAuA+ERkKPAQsNMYMAhaGXkNwst6g0Nc9dOJ5GB9++BFkFxDIKjzpPU9BH7Zv28bevXuTUJlS6WXy5Mn4fV5u6h+7lQ6u7esi1w4vPP88xpx+1nYqi2tIhC5urwo9dwCbgF7A9cArod1eAW4IPb8e+LsJWgYUisgZ8awxGWpqalizZjXuwr60tMSkr6gcgA8++CCxhSmVZiorK5k/fx7fKHNRkh2I2efm2g0TyhtYvWZNU69BR5WwaxIiUg6cA3wGdDfG7INgkAClod16AbubHVYV2tapfPjhhwQCAXxF/Vp832TmE8grYeHCRQmuTKn08te//oVcO1zXN/YrL1/es5EeuYa//uUFfL62XwxPtoSEhIjkAW8CPwst7RFx1xa2ndRWE5F7RGSFiKw4ePBgrMpMmEWLFkF2IYHsooj7eLr2Y9u2Snbv3h1xH6VU261evZrPP1/BN/s0kNuKhfyiZbPAjf3q2blrNwsWLIj55ydK3ENCROwEA2KqMWZWaPOBcDdS6DE886QK6N3s8DLgpI55Y8wkY8woY8yokpKS+BUfB4cOHWLt2rW4u/ZrsaspLNzKWLRIWxNKxZoxhr+9PJmuWTCmV/Szq1vrvBIPffMD/OPvr3TY1kS8RzcJMBnYZIz532ZvzQZuCz2/DXi72fZbQ6OcLgBqw91SncVHH32EMSZiV1OYycjFn9+DRYsXJ6gypdLHunXrWLtuPeN7N5BhPf3+bSUCE8rr2btvP4s76M9yvFsSFwM/BK4QkTWhr3HA74FviMhW4Buh1wDvAtuBSuBF4N/iXF/CLVy0CJNTRCD75FFNJ/J2LWfnl1/y5Zdfxr8wpdLIm2++Sa4dvtYzfq2IsBHdvPTINcx68424nyse4j26aYkxRowxZxtjRoS+3jXGHDbGjDHGDAo9Hgntb4wx9xljBhhjzjLGdKoZZYcOHWLjhg14upa3an9faD8d5aRU7NTU1PDxxx9zWQ8XmVG0Il7dksNOh5WdDiu/W9WFV7fktOo4i8CVPZ1s2lzBtm3b2lh18uiM6wRavHgxxhi8XU/d1RRmMnLw5/fg/YULO/xYa6VSxSeffEIgEODCHtGtj7ar3obLb8Hlt7C5xs6u+tavJX5Bdzciwe7mjkZDIoHmz38Pk9sNk13Q6mO8Rf2p2r2bioqKOFamVPpYtmwZ3bKhbxtWem2rLhmGMwt8fNoB71ynIZEgW7ZsobJyK+7igVEd5y3qh1htzJ07N06VKZVeKjZvYmAX96kGF8bFwC5eduzYgcfjSeyJ20lDIkFmzZqFWO14iwdEd6AtE0/Xfrz33gLq6k41xUQpdToNDQ1UHzxE79zED0ftm+/D5/dTVVWV8HO3h4ZEAlRXV7NgwQLcxYMgdB/raHi6D8PtbuSf//xnHKpTKn2E/9AqyEj8Nb4u9uCyHx3tHvYaEgnw8ssv4zfg6fHVNh0fyCnCV9iH6dNnUFNTE+PqlEofLldw+Y1Ma+JDIjN0ndvpjN1CgomgIRFnW7duZf78+XhKhmAy89r8Oe6ykbgaXfztb3+LYXVKpZecnOCwVbc/wRckALfv+Bo6Cg2JOPL7/Tz55FNgz8Ldc0S7PiuQ3RVPyRDenj1b71qnVBvl5+cD4PAmPiQcXstxNXQUGhJxNGvWLCoqNuPsdV6brkWcyN1rJGLP5vd/+B+8Xm8MKlQqveTm5lKQn8cBVxzX4oggfM4zzuhYdz/QkIiTqqoqJr34Ir6C3viiHdEUiS2Dhj4X8eWO7bz66qux+Uyl0kzvPn3Z52z9RLhY2dtgpaS4iOzs7ISfuz00JOLA5/Px28cfx2eExvKLT7naa7T8XfvgLR7AP/7xD+12UqoN+vXvT1WDnUQvYlDltNN/YHTzpFKBhkQczJw5k82bNuHsfQEm49QXqTJ3LcPqPIzVeZjsze+SuWvZaT+/sc8FBOw5PP7473C7o1taQKl0179/fxq8hhpP4q5LBAzsbbDQr1//hJ0zVjQkYmznzp1MfvllfF3L8RWd/hvC4jyC+L2I34vNsR+L88jpT2LLxNn3Inbv3sWUKVPaX7RSaaRnz54AHGpM3HWJGo8FX6DjXY8ADYmYMsbw5FNPERArjX0vjGk304n8BWV4ug1ixowZbN++PW7nUaqz6datGwBH3Yn79VfjluPO3ZFoSMTQwoULWbd2La6eIzH2+F+c8pSdh7Fm8PTTz+gqsUq1UiAQnPlslcT9zFjk+HN3JBoSMeLz+XjxpcmY3GK8JYMTck5jz8J1xgjWrv2Czz//PCHnVKqjO3Ik2KWbbUtcSOSEznX06NGEnTNWNCRiZNGiRRzYvw9Xz3Pi2s10Im/JYMjM45VX/p6wcyrVkS1fvhy7FQZ0SdwifyVZAYqy6JB/zGlIxMibs2ZBdiH+gt6JPbHFSmPpUDZsWE9lZWViz61UB1NXV8fCBe8xtNAT1V3p2ksEzilu5LNlS9mzZ0/iThwDGhIxsH37dio2b6ax2+CEtiLCvN0GIRYr7777bsLPrVRH8vzzz1PncHBj/8QvsvfNchdWfDz5xBMd6hqoK/1bAAAQvklEQVSihkQMLFiwAETwFSdpDLQtE09Bbxa8v1CX61AqgmnTpjFv3jzG93HSNz9xd6ULK8oMcHP/elavWcMzzzzTYS5ia0i0k8fjYc7cd/EV9E7IiKZIvN0G4airZcmSJUmrQalUZIxhypQpTJo0iQu6u/lWP1fSavl6Tzfj+rh46623eOqpp/D7Ex9W0dKQaKe5c+fiqKvFU/qVpNbhL+gFWV14depUfL7E33VLqVRUW1vLb/7zP5kyZQqX9mjkX4bWY03ibz0R+O4AJ9eVO5k7dy733/8zqqurk1dQK2hItMO+ffv4y1//ir9LT/xdeia3GLHg6jWSbZWVzJgxI7m1KJUCVq9ezV133sGnny7h5oEN3PWVhqb5CskkAjf2d3HvVxxs2bieO++4nQ8++CDZZUWU+KUQO4mqqip+/vMH8PgCuM6M7SJ+beXrWo6vazkvvvgiOTk5TJgwIdklKZVwNTU1/OUvf2HevHn0yDU8PLKW8iRcgzidi8/wMLDgCC9s7MIjjzzCRRddxE9+8hN69OiR7NKOoy2JNli2bBn3TZzIwaO11J95DSYzRW4iIoKr/2X4CvvwzDPP8NxzzzXdrlGpzi4QCDBnzhx++IPvs2D+PMb3cfFfo46kZECEdc8J8Otza/jugAZWfvYpt912K9OmTUupASgaElE4dOgQDz/8MA899BA1bqgfPJZAbjvXYvF7yMrK4sYbbyQrKwv8nvZ9nsWGa8AVeEqH8Prrr3PrbbexbNnpV5ZVqiOrqKjgvn/7V5588kl62mr5r/Nq+O5AZ0LnQrSVzQLj+zby36OPMiy/nkmTJnHXnXewcuXKZJcGaHdTqxw+fJgZM2bwz7fewufz4+51Lp4eZ4Gl/d+B4vNw7XXXMnHiRIwxzHxnfvsLtlhw970IX1F/qnd+ykMPPcTw4cO5/fbbGTFiBJICXWNKxUJdXR0vvfQS77wzm/wMuPsr9VzSw50Kvb9R65Yd4GdnO1h9yM7Uyt088MADfO1rX+O+++6jtLQ0aXVpSJxCdXU1M2bMYPbs2Xh9PrxF/XH3PAeT1SVm5zC2DObMmYMxhrlz52JssbtJuj+/B/VDr8devZm1m9Zx//33M+yrX+W2W2/lvPPO07BQHdqyZct44g+/52hNDd/o5WJCPxe59o4zSS2Sc7p5Gdb1CO/uyuadJR+y4vPl/OSnP+Oqq65Kys+sdKSZfy0ZNWqUWbFiRUw/c9euXUybNo0FCxbgDxg8xQPwnDE8puEQlr35XWyO/U2vffk9cA0ZF/PzEPBhP7iFrAPrwV3PgAED+cEPvs9ll12G1doB2uRKhTidTp577jnmzp1Lr7wA936lLiHXHX63qguba+xNr4cUevnVuXVxPecBp4UXN+ezpcbGJRdfzC8efJDCwsKYfLaIrDTGjDrdftqSaObgwYO88MILLFq8GBEr7m5n4ulxFiYzL9mltZ/Fhrf7ULwlg7Ef3sa2vet49NFH6dmrFxPvu4+LLroo2RUqdVr19fX8vwd/wabNmxnfx8W3+juxd+Irq91zAvzqnFrm787ijaWfMPG+HTz9zJ8Sel+KlPvnFZFrRKRCRCpF5KFEnNPn8zFz5kx+8MMfsuiDD3F3PwvH2Tfh7nth5wiI5ixWvCVn4hg2AdeAy9lzpIFf/epX/Oo//oP9+/ef/nilkqS+vp4Hf/EAFZs3M3GYg+8O7NwBEWYRGNunkYfOqeXwgX389Cc/5uDBg4k7f8LO1AoiYgWeA8YCQ4FbRGRovM/75z//meeff56GzG7UD5uAp/eopC6xkRBiwVfUj/qh1+MuG8XSZZ9xzz33Ultbm+zKlGrR1KlTqaioYOJX6zivtJ2jADugQQU+fjG8hsPV+3n++ecTdt6UCglgNFBpjNlujPEA04Hr43nCiooK3p49G0/pUFyDvhGX6w4pzWLFc8bZ1A8ZT52jjsmTJye7IqVOEggEeH/Be5xV5GFkSXLmELh8ctxwdZcv8ReRBxX4uLi7i08/WYLTmZiVbFMtJHoBu5u9rgptO46I3CMiK0RkRXubXZs2bQJjCGQXpsSs6WQx9hywZbF+/fpkl6LUSY4ePcrBQ4cZmMAbBZ3I6ROuvTY4XH38+PE4kxASAAO6eHF7vOzYsSMh50u1C9ct/aufNPzKGDMJmATB0U3tOeG1117LBx9+yJovPkP8Hjwlg8GW2Z6P7FiMwVq3h6yqFdjFzy9/+ctkV6TUSYqLixl85iCW7tnCdeWupPw9l2Mzxw1X757A2582t/RAFsVFXRk8ODG3SU61lkQV0PzWbmXA3nie0Gaz8egjjzDynHPIrFpBl7Uzydz1GeKuj+dpmwRyivDl92j6CuQUJeS8BPzYDm0lb+Pb5Gx5j+Is4ZGHH2bQoEGJOb9SUfr2jText0GYvDkXXxJuxTC40Et3Wz0fzZ1Jd1s9gwsT2+1lDLyxLZt1R+xcf8MEbLbE/I2fUvMkRMQGbAHGAHuAz4HvGWM2RDomlvMktm7dyvTp01m8eDGBQIBAXinewj54C/tisgtico6k8nuw1VRhq9lJRt0ejM9D3/Jybrn5ZsaMGYPdbj/9ZyiVJOH7QrzyyisMLfIxcVgdeZ1g8lxruP0weXMeyw5kMm7cOH7+85+3OyRaO08ipUICQETGAU8DVuBlY8zjp9o/HpPpDhw4wHvvvcdHH3/M1i1bghtzuuIu6I2/oDf+vBKQVGuEtUzc9dhqq7DV7MLm2AcBP/ldCrjs0ku4/PLLGTlypM68Vh3KvHnzePKJJ8iy+rmhbwNX9GrE1jF+HKMWMLDsQAYzt+dzpBHuvvtuvve978XkZ7bDhkS04hESzVVXV7NkyRI++vhj1n7xBYFAALFl4sk/A19BGf6CMkxG7JbSaLeAH6tjP7baKjIce8F5FIDS7j34+tcu45JLLmHYsGE6y1p1aNu2beO5555l1arV9Mw1fHeAgxHF3k419qSixsZrlXlsr7MyaOBAJv74xwwfPjxmn68hEQcOh4OVK1eyfPlyli5bxtEjRwAwucV4upThK+wTXBU2wd+p4nViq9mNrWYXdsd+jN+L1WZjxPDhXHDBBYwePZo+ffpoi0F1KsYYli5dyvPPPUvVnr30yQ8wvncDo0s9Sb37XHsYA2sP25mzK4eKGhvFRV25+557ueqqq7BYYvsfpSERZ8YYtm/fzvLly/l06VLWr1uHMQbJyMHdpQxf1z7Bu9VZ4nBxyRgsrqPBUKjdjaU+OAy4pLQ7F190Ieeffz4jRowgO7uTTwhUiuCKCQsWLOC1aVPZtbuKkhzD2LIGLj3D3SGWCgfwBeCz6gze3ZXL7noLJd2K+e7NtzB+/Pi4/RxrSCRYbW0tn332GZ9++inLPvuMRpcLsWXi7lqOt9sgArkl7W5hiNeF7fA2Mg9XIs5gK2bwkCFccvHFXHzxxfTr109bCyptBQIBPv30U6ZNfZWNmzaTa4fLe7q4sqyRoswkDIdqBYdX+GBPJu/vzeVoI5T37cMt3/s+Y8aMifvoJQ2JJPJ6vaxZs4YFCxbwwQcf4vG4IbsQd/FAPKVDwJrR+g8LzWPIqN6ErbYKjGHwkCGMGzuWSy65hOLi4vj9hyjVARljWLduHa+//jqfLFmCYDiv1M01vV3075Iad6nb22Bh/u5sPjmQhccPo0aey403fYfRo0fHvFspEg2JFOF0Ovnwww95993/Y926tYg9C1ePs/GWDjltV5TVsZ+sPSuxOA5Q2LWIsddczTXXXEPfvn0TVL1SHdu+ffuYNWsWc+e8g9PVyFe6+hjXx8nZRcm5yL2lxsbcXdmsPpSB3W7jqquu5tvf/jb9+/dPeC0aEilo8+bNTHrxRVatXIlk5BCwn2JUlPEjzqMUdu3KHbffzrhx43Qeg1Jt1NDQwNy5c3l95gwOHjpMWV6Acb0buLB7/C9yGwOrDtmZuyuXylorXfLzmPCtbzNhwoSY3RuiLTQkUtjq1auZPXs2jY2Np9zv7LPPZsKECcF7Xyul2s3r9bJ48WJemzaVHV/upEeu4Vvl9Ywu9WCJccvCGFh3xM6bO3LZUWfljO6lfOfmWxg7dmxK/ExrSCilVATGGJYsWcLLk19ix5c76ZMf4Kb+9Qwvjs1SG1trbczclktFjY0e3Uu5/Y47ufLKKxO2lEZraEgopdRp+P1+Fi9ezMuTX2Lvvv2cV+Lmh2c2UJjZtt+LTp8wc1sOi/ZkUdS1kFtvu53x48enZFexhoRSSrWSz+djxowZTJnyN+z4+N6Aei49wx3Vxe3Vh+xM2dKFWrdw4003cccdd6T0XCW9x7VSSrWSzWbj+9//PpdeeilPPvE/vLRuPdvqbPzwzIbTrgtlDLz1ZTb/3JFD/37l/Pe/P8SQIUMSUnciaEgopVRInz59ePqZPzF58mSmTp1KdaOVy3ueeoDJiuoMllVncvXVV/PAAw+QkRHFPKgOQENCKaWasVgs3H333ZSVlfHUk0+y4cjpryfEcnXWVKMhoZRSLRg7diznn38+tbW1p9wvNzeX0tLSBFWVeBoSSikVQVFREUVFCbpbZIrqoAvqKqWUSgQNCaWUUhFpSCillIpIQ0IppVREGhJKKaUi0pBQSikVkYaEUkqpiDr8An8ichDYmew6OpFuwKFkF6FUC/R7M7b6GmNKTrdThw8JFVsisqI1K0MqlWj6vZkc2t2klFIqIg0JpZRSEWlIqBNNSnYBSkWg35tJoNcklFJKRaQtCaWUUhFpSCgAROQaEakQkUoReSjZ9SgVJiIvi0i1iKxPdi3pSENCISJW4DlgLDAUuEVEhia3KqWaTAGuSXYR6UpDQgGMBiqNMduNMR5gOnB9kmtSCgBjzEfAkWTXka40JBRAL2B3s9dVoW1KqTSnIaEAWrp7uw57U0ppSCgg2HLo3ex1GbA3SbUopVKIhoQC+BwYJCL9RCQDuBmYneSalFIpQENCYYzxAROB+cAmYKYxZkNyq1IqSEReA5YCg0WkSkTuSnZN6URnXCullIpIWxJKKaUi0pBQSikVkYaEUkqpiDQklFJKRaQhoZRSKiJbsgtQKlWJSDGwMPSyB+AHDoZejw6tcxXrc54LlBpj5sX6s5VqCw0JpSIwxhwGRgCIyCNAvTHmydYeLyJWY4w/ytOeC3wV0JBQKUG7m5RqAxF5R0RWisgGEflRaJtNRGpE5LcishwYLSLXhe7T8bGI/FlE3grtmyciU0RkuYisFpFvikg28Bvg+yKyRkRuTOJ/olKAtiSUaqvbjDFHRCQHWCEibwIOoABYZYz5dei9LcDFwC5gZrPjfwPMM8bcLiJdgc+As4HHgK8aY36WyP8YpSLRloRSbXO/iHxBcLmIMmBAaLsH+Gfo+VCgwhiz0wSXNnit2fFXAf8hImuAxUAW0CchlSsVBW1JKBUlEbkSuAy4wBjjEpElBH/JA7jMsbVuWlqCnWbv3WCM2XbCZ18W84KVagdtSSgVvQLgSCgghgHnRdhvA8FF6XqLiADfbfbefOAn4Rcick7oqQPIj0PNSrWJhoRS0ZsL5IS6m35D8HrCSYwxToKr674PfEzwHh21obcfDX3GOhHZADwS2r4IGB66mK0XrlXS6SqwSsWRiOQZY+pDLYm/AuuMMX9Odl1KtZa2JJSKr38NXZzeCGQDLya5HqWioi0JpZRSEWlLQimlVEQaEkoppSLSkFBKKRWRhoRSSqmINCSUUkpFpCGhlFIqov8PrBLiDuEBHnkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='serum_insulin',data=train,hue='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\install\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db9d809e8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xR4AvAG8A944gnzQUTr9on1NV/wisB6bm7PsRsB24gcGqldJEcqSufU6Sk4H9GCyedNCch/4E+Luq+v5gAUtp8ljq2lfsmVOHwYdTbK6q9+aWd1U9h1e9aMK5SqMkNcQ5dUlqiKUuSQ2x1CWpIZa6JDXEUpekhljqktQQS12SGmKpS1JD/hcrTevRwP8/MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI\n",
    "sns.distplot(train.BMI,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\install\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db9cd75c0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='BMI',data=train,hue='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db9e87278>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI', 'Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db9c709b0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Anfz0FfVNPxeG7INVm3bQDzNVwo/aVNWDwAPAY/dIdXvGsNNF/Fr/p+qVSY5eZHvLnFKj84wkn0nyd0l+btrFTFI/RPs0YNuCTfvNc2GZPoBVPhemHfQrTpUwZJt92TCP74PAhqp6CvAP/PgvnP1F68+BYdwKPL6qfh74C+B/TrmeiUnySOAq4Leq6lsLNy9yl+aeCyv0waqfC9MO+hWnShhsk2Qd8Bja+hN3mOkivl5V3+9vvgf4hT1U295imOdJ06rqW1X1nX75WuDAJOunXNaaS3IgXcBdUlVXL9Kk+efCSn0wynNh2kE/zFQJW4GN/fJZwEeqf0eiESv2wYIxyNPpxu32J1uBl/WfuDgJeKCq7pt2UXtSkp+Zf28qyYl0/3e/Pt2q1lb/+C4C7qyqP1uiWdPPhWH6YJTnwlS/SrCWmCohyR8A26tqK92D/uskO+jO5M+eXsVrb8g+eE2S04EH6frg3KkVPAFJLqP7JMH6JLuAC4ADAarq3XRXWJ8G7AC+C7x8OpVOzhB9cBbwn5M8CHwPOLuxEx6AZwEvBT6b5NP9ujcDx8B+81wYpg9W/VzwylhJaty0h24kSRNm0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6g109I8sN+6tM7+rk0Xp/kIf222SQXrnD/c5P85SqP+eZxal5rSW5IMtsvX5vkkCnVcVk/v9Hr1nCfJyd55sDtVyR52VrtX3unqV4wpb3S96rqqQBJDgcupZt24oKq2g5sn8Ax3wz88QT2O7aqOm017ZOs6yffG0uSnwGeWVWPH3dfC5wMfAf4BPzoAhw1zjN6LamqdgObgFf1l5yfnORD0F16neQTST7V/37iwF2PTvL36b5M5YL5lUl+Pckn+78Y/keSA5K8BTi4X3fJMu0OSPK+JLcn+exyZ7n9Gfnb+7pu7y8TJ8kj0n3Bx8193Wf06w9O8oH+7Ply4OCBfd0zP49Ikt9N8vkk1/Vn228YON4fJ/kY8NokM0mu6o9zc5JnLXf8JXwYOLzvg19c8FfG+iT39MvnJrm67++7kvzJQO2nJrm1/8vs+nSzIb4CeN3Afn9/4HE8NclNfT/8bZJDBx7fW/t/ky8m+cVl6tbeaNoT7fuzd/0A31lk3f3A4xj4wgPg0cC6fvm5wFX98rnAfXRTSR8M3A7MAk+im4XzwL7dO4GXLTzmUu3oJnK7bqDdIcs8hhuA9/TLz6H/Mg+6vxp+ff7+dF/q8Ajg9XRTTwA8hW6qidn+9j3A+v4xfLp/TI8C7gLeMHC8dw4c/1Lg2f3yMXTzlix5/CUewwYGvoSkP8Z8TeuBewb6+266v7oOAr5MN+nXDN10vsf27Q7rf//+fN0LbwO3Ab/UL/8B8PaBY//3fvk04B+m/Tz1Z3U/Dt1oGItNDfsYYEuS4+imiT1wYNt1VfV1gCRXA8+mC89fAG5ONx/TwcDuRfZ7yhLtPgg8IclfAP+L7ox3OZdB94UeSR7dj7P/O+D0+TNYumA8hu7F4MK+/W1Jbltkf88Grqmq7/WP64MLtl8+sPxc4Pj8+PtxHp3kUcscf9xJ6q6vqgf6uj4HPB44FLixqr7UP65lZ3xN8hi6F8+P9au2AH8z0GR+FsVb6F6EtA8x6LWsJE8AfkgXtk8a2PSHwEer6lf7IYEbBrYtnECp6F4stlTVm1Y65FLtkvw88HzglcB/AH5jmf0sVcOvVdUXFux3sfaL1bWcfxlYfgjwjPkXhYHjLHr8IT3Ij4daD1qw7fsDyz+k+38d1nae9vljzO9f+xDH6LWkJDPAu+m+r3ZhaDwG+Gq/fO6Cbc9LcliSg4Ezgf8DXA+c1b/BS799/o3GH6Sbg5ul2vXj5A+pqquA36X7ftXl/Mf+/s+mm8r2AboZQl/dBy5Jnta3vRE4p1/3ZLrhm4U+Dvz7JAel+1KIX1nm2B8GXjV/I8lT+8Wljj+Me/jx9xCcNUT7fwR+Kcmx/bEO69d/m27o6Sf0/XP/wPj7S4GPLWynfZOvzFro4HTTox5Idxb518Bi82L/Cd3QzeuBjyzY9vH+fj8LXFrdp3VI8l+BD6f7uOYP6M7MvwxsBm5LcmtVnbNEu+8B7+3XAaz0l8H9ST5B917C/Jn/HwJv748VuvB8IfCuft+30Y3Df3Lhzqrq5iRb6b68/ct0nz56YIljvwZ4R7+/dXQvJK9Y5vjD+FPgiiQv5af7+6dU1VySTcDVfZ/tBp5HNwR2Zf9G8KsX3G0j8O4kD6cb929tCuD9ltMUqzlJbqB7g3FNPwqa5JFV9Z0+CG8ENlX/Rc7S3swzeml4m5McTzdGvsWQ177CM3rts5K8g+4beQb9eVW9dxr1jCLJ84G3Llj9par61WnUozYZ9JLUOD91I0mNM+glqXEGvSQ1zqCXpMYZ9JLUuP8PCYEKqvQUjawAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Diabetes_pedigree_function\n",
    "sns.distplot(train.Diabetes_pedigree_function, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db9cefdd8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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50bhrkUbBoJe6A3S+QHcQmNQcg15Htf5cSufTnS72ir7tGUk+0J9L/PYkdyS5rL/v5Un+tT8R2mfnDs2XJplBr6PdpXTncf8P4JEkZwG/C6wDfo3uaMbz4BfnXvoH4LKqejlwHXDNOIqWlmJkPw4uTYnNdCdmg+7Q9M3AM4FPVdXPge8luau//3TgTODO7vQlHEN3Clppohn0Omr151C5EDgzSdEFd/HEOXCe8hDg/qo67wiVKA2FUzc6ml0GfKyqXlBV66pqLd0vNT0M/F4/V38ycEHffw8wk+QXUzlJXjKOwqWlMOh1NNvMU7feb6L7oYhZutMWf5DujKmPVtXP6P44vCfJV4F76c4xLk00z14pLSDJc6rqx/30zt10v0b1vXHXJS2Hc/TSwm7vfyzmWcBfG/KaZm7RS1LjnKOXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/BtPpG+2qagEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#age\n",
    "sns.distplot(train.Age, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23db7afe1d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
